Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

Separation of Cs/Sr is one of many coordination-chemistry-centered
processes in the grand scheme of spent nuclear fuel reprocessing,
a critical link for a sustainable nuclear energy industry. To deploy
a crystallizing Cs/Sr separation technology, we planned to systematically
screen and identify candidate ligands that can efficiently and selectively
bind to Sr2+ and form coordination polymers. Therefore,
we mined the Cambridge Structural Database for characteristic structural
information and developed a machine-learning-guided methodology for
ligand evaluation. The optimized machine-learning model, correlating
the molecular structures of the ligands with the predicted coordinative
properties, generated a ranking list of potential compounds for Cs/Sr
selective crystallization. The Sr2+ sequestration capability
and selectivity over Cs+ of the promising ligands identified
(squaric acid and chloranilic acid) were subsequently confirmed experimentally,
with commendable performances, corroborating the artificial-intelligence-guided
strategy.